R Tutorial
An introduction to R
Introduction
This tutorial is will introduce the reader to
,
a free, open-source statistical computing environment often used with
RStudio, a integrated development environment for
.
R Project Logo
Download
Download at https://www.r-project.org/
Download RStudio at https://rstudio.com/products/rstudio/download/
Calculator
can be used as a super awesome calculator
# 5 + 3 = 8
5 + 3 ## [1] 8
# 24 / (1 + 2) = 8
24 / (1 + 2) ## [1] 8
# 2 * 2 * 2 = 8
2^3 ## [1] 8
# 8 * 8 = 64
sqrt(64) ## [1] 8
# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000) ## [1] 8
Functions
has many useful built in functions
1:10## [1] 1 2 3 4 5 6 7 8 9 10
as.character(1:10)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
rep(1:2, times = 5)## [1] 1 2 1 2 1 2 1 2 1 2
rep(1:5, times = 2)## [1] 1 2 3 4 5 1 2 3 4 5
rep(1:5, each = 2)## [1] 1 1 2 2 3 3 4 4 5 5
rep(1:5, length.out = 7)## [1] 1 2 3 4 5 1 2
seq(5, 50, by = 5)## [1] 5 10 15 20 25 30 35 40 45 50
seq(5, 50, length.out = 5)## [1] 5.00 16.25 27.50 38.75 50.00
paste(1:10, 20:30, sep = "-")## [1] "1-20" "2-21" "3-22" "4-23" "5-24" "6-25" "7-26" "8-27" "9-28" "10-29" "1-30"
paste(1:10, collapse = "-")## [1] "1-2-3-4-5-6-7-8-9-10"
paste0("x", 1:10)## [1] "x1" "x2" "x3" "x4" "x5" "x6" "x7" "x8" "x9" "x10"
min(1:10)## [1] 1
max(1:10)## [1] 10
range(1:10)## [1] 1 10
mean(1:10)## [1] 5.5
sd(1:10)## [1] 3.02765
Custom Functions
Users can also create their own functions
customFunction1 <- function(x, y) {
z <- 100 * x / (x + y)
paste(z, "%")
}
customFunction1(x = 10, y = 90)## [1] "10 %"
customFunction2 <- function(x) {
mymin <- mean(x - sd(x))
mymax <- mean(x) + sd(x)
print(paste("Min =", mymin))
print(paste("Max =", mymax))
}
customFunction2(x = 1:10)## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"
for loops and if else
statements
xx <- NULL #creates and empty object
for(i in 1:10) {
xx[i] <- i*3
}
xx## [1] 3 6 9 12 15 18 21 24 27 30
xx %% 2 #gives the remainder when divided by 2## [1] 1 0 1 0 1 0 1 0 1 0
for(i in 1:length(xx)) {
if((xx[i] %% 2) == 0) {
print(paste(xx[i],"is Even"))
} else {
print(paste(xx[i],"is Odd"))
}
}## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"
# or
ifelse(xx %% 2 == 0, "Even", "Odd")## [1] "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even"
paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))## [1] "3 is Odd" "6 is Even" "9 is Odd" "12 is Even" "15 is Odd" "18 is Even" "21 is Odd" "24 is Even" "27 is Odd"
## [10] "30 is Even"
Objects
Information can be stored in user defined objects, in multiple forms:
c(): a string of valuesmatrix(): a two dimensional matrix in one formatdata.frame(): a two dimensional matrix where each column can be a different formatlist():
A string…
xc <- 1:10
xc## [1] 1 2 3 4 5 6 7 8 9 10
xc <- c(1,2,3,4,5,6,7,8,9,10)
xc## [1] 1 2 3 4 5 6 7 8 9 10
A matrix…
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 2 3 4 5 6 7 8 9 10
## [2,] 11 12 13 14 15 16 17 18 19 20
## [3,] 21 22 23 24 25 26 27 28 29 30
## [4,] 31 32 33 34 35 36 37 38 39 40
## [5,] 41 42 43 44 45 46 47 48 49 50
## [6,] 51 52 53 54 55 56 57 58 59 60
## [7,] 61 62 63 64 65 66 67 68 69 70
## [8,] 71 72 73 74 75 76 77 78 79 80
## [9,] 81 82 83 84 85 86 87 88 89 90
## [10,] 91 92 93 94 95 96 97 98 99 100
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
A data frame…
xd <- data.frame(
x1 = c("aa","bb","cc","dd","ee",
"ff","gg","hh","ii","jj"),
x2 = 1:10,
x3 = c(1,1,1,1,1,2,2,2,3,3),
x4 = rep(c(1,2), times = 5),
x5 = rep(1:5, times = 2),
x6 = rep(1:5, each = 2),
x7 = seq(5, 50, by = 5),
x8 = log10(1:10),
x9 = (1:10)^3,
x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
A list…
xl <- list(xc, xm, xd)
xl[[1]]## [1] 1 2 3 4 5 6 7 8 9 10
xl[[2]]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
xl[[3]]## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
Selecting Data
xc[5] # 5th element in xc## [1] 5
xd$x3[5] # 5th element in col "x3"## [1] 1
xd[5,"x3"] # row 5, col "x3"## [1] 1
xd$x3 # all of col "x3"## [1] 1 1 1 1 1 2 2 2 3 3
xd[,"x3"] # all rows, col "x3"## [1] 1 1 1 1 1 2 2 2 3 3
xd[3,] # row 3, all cols## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"## x4 x5
## 2 2 2
## 4 2 4
xl[[3]]$x1 # 3rd object in the list, col "x1## [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"
regexpr
xx <- data.frame(Name = c("Item 1 (detail 1)",
"Item 20 (detail 20)",
"Item 300 (detail 300)"),
Item = NA,
Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx## Name Item Detail
## 1 Item 1 (detail 1) Item 1 detail 1
## 2 Item 20 (detail 20) Item 20 detail 20
## 3 Item 300 (detail 300) Item 300 detail 300
Data Formats
Data can also be saved in many formats:
- numeric
- integer
- character
- factor
- logical
xd$x3 <- as.character(xd$x3)
xd$x3## [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"
xd$x3 <- as.numeric(xd$x3)
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
xd$x3 <- as.factor(xd$x3)
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3
xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1
xd$x10## [1] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
as.numeric(xd$x10) # TRUE = 1, FALSE = 0## [1] 1 1 1 0 0 1 1 0 0 0
sum(xd$x10)## [1] 5
Internal structure of an object can be checked with
str()
str(xc) # c()## num [1:10] 1 2 3 4 5 6 7 8 9 10
str(xm) # matrix()## int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
str(xd) # data.frame()## 'data.frame': 10 obs. of 10 variables:
## $ x1 : chr "aa" "bb" "cc" "dd" ...
## $ x2 : int 1 2 3 4 5 6 7 8 9 10
## $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
## $ x4 : num 1 2 1 2 1 2 1 2 1 2
## $ x5 : int 1 2 3 4 5 1 2 3 4 5
## $ x6 : int 1 1 2 2 3 3 4 4 5 5
## $ x7 : num 5 10 15 20 25 30 35 40 45 50
## $ x8 : num 0 0.301 0.477 0.602 0.699 ...
## $ x9 : num 1 8 27 64 125 216 343 512 729 1000
## $ x10: logi TRUE TRUE TRUE FALSE FALSE TRUE ...
str(xl) # list()## List of 3
## $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
## $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
## $ :'data.frame': 10 obs. of 10 variables:
## ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
## ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
## ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
## ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
## ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
## ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
## ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
## ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
## ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
## ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...
Packages
Additional libraries can be installed and loaded for use.
install.packages("scales")library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx## Values Rescaled
## 1 1 1.000000
## 2 2 4.222222
## 3 3 7.444444
## 4 4 10.666667
## 5 5 13.888889
## 6 6 17.111111
## 7 7 20.333333
## 8 8 23.555556
## 9 9 26.777778
## 10 10 30.000000
libraries can also be used without having to load them
scales::rescale(1:10, to = c(1,30))## [1] 1.000000 4.222222 7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000
Data Wrangling
R for Data Science - https://r4ds.had.co.nz/
xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
Data1 = 1:10,
Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 X 6 60 66 6000
## 7 X 7 70 77 7000
## 8 X 8 80 88 8000
## 9 Y 9 90 99 9000
## 10 Y 10 100 110 10000
xx$Data1 < 5 # which are less than 5## [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
xx[xx$Data1 < 5,]## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx[xx$Group == "X", c("Group","Data2","NewData1")]## Group Data2 NewData1
## 1 X 10 11
## 2 X 20 22
## 6 X 60 66
## 7 X 70 77
## 8 X 80 88
Data wrangling with tidyverse and pipes
(%>%)
library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
mutate(Data1 = 1:10,
Data2 = seq(10, 100, by = 10),
NewData1 = Data1 + Data2,
NewData2 = Data1 * 1000)
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 Y 6 60 66 6000
## 7 Y 7 70 77 7000
## 8 X 8 80 88 8000
## 9 X 9 90 99 9000
## 10 X 10 100 110 10000
filter(xx, Data1 < 5)## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx %>% filter(Data1 < 5)## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx %>% filter(Group == "X") %>%
select(Group, NewColName=Data2, NewData1)## Group NewColName NewData1
## 1 X 10 11
## 2 X 20 22
## 3 X 80 88
## 4 X 90 99
## 5 X 100 110
xs <- xx %>%
group_by(Group) %>%
summarise(Data2_mean = mean(Data2),
Data2_sd = sd(Data2),
NewData2_mean = mean(NewData2),
NewData2_sd = sd(NewData2))
xs## # A tibble: 2 × 5
## Group Data2_mean Data2_sd NewData2_mean NewData2_sd
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 X 60 41.8 6000 4183.
## 2 Y 50 15.8 5000 1581.
xx %>% left_join(xs, by = "Group")## Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1 X 1 10 11 1000 60 41.83300 6000 4183.300
## 2 X 2 20 22 2000 60 41.83300 6000 4183.300
## 3 Y 3 30 33 3000 50 15.81139 5000 1581.139
## 4 Y 4 40 44 4000 50 15.81139 5000 1581.139
## 5 Y 5 50 55 5000 50 15.81139 5000 1581.139
## 6 Y 6 60 66 6000 50 15.81139 5000 1581.139
## 7 Y 7 70 77 7000 50 15.81139 5000 1581.139
## 8 X 8 80 88 8000 60 41.83300 6000 4183.300
## 9 X 9 90 99 9000 60 41.83300 6000 4183.300
## 10 X 10 100 110 10000 60 41.83300 6000 4183.300
Read/Write data
xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)For excel sheets, the package readxl can be used to read
in sheets of data.
library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")Tidy Data
Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html
Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html
yy <- xx %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = round(mean(DTF),1)) %>%
arrange(Location)
yy## # A tibble: 9 × 3
## # Groups: Name [3]
## Name Location Mean_DTF
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
## <chr> <dbl> <dbl> <dbl>
## 1 CDC Maxim AGL 86.7 134. 52.5
## 2 ILL 618 AGL 79.3 138. 47
## 3 Laird AGL 76.8 137. 56.8
yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy## # A tibble: 9 × 3
## # Groups: Name [3]
## Name TraitName Value
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
yy <- yy %>% spread(key = Name, value = Value)
yy## # A tibble: 3 × 4
## TraitName `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
## <chr> <dbl> <dbl> <dbl>
## 1 Jessore, Bangladesh 86.7 79.3 76.8
## 2 Metaponto, Italy 134. 138. 137.
## 3 Saskatoon, Canada 52.5 47 56.8
Base Plotting
We will start with some basic plotting using the base function
plot()
Tutorial 1 - http://www.sthda.com/english/wiki/r-base-graphs
Tutorial 2 - https://bookdown.org/rdpeng/exdata/the-base-plotting-system-1.html
# A basic scatter plot
plot(x = xd$x8, y = xd$x9)# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)# Adjust plot type
plot(x = xd$x8, y = xd$x9, type = "line")# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")Now lets create some random and normally distributed data to make some more complicated plots
# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru## [1] 0.813247 66.461884 57.473977 70.892271 68.556828 14.448441 88.168352 32.491287 89.298329 20.100106 64.040092 46.788096
## [13] 40.587486 58.206649 3.515963 34.233539 69.411440 82.002651 88.701946 50.471673 29.143809 46.478278 82.500989 84.414478
## [25] 95.701496 96.304172 13.571551 79.266511 9.519536 75.180364 88.644241 3.370775 20.574142 49.107972 94.263583 52.874291
## [37] 78.062900 73.886750 28.231896 23.305806 57.226434 66.010886 6.247110 56.235010 70.654066 24.520998 43.842966 98.712801
## [49] 7.269671 35.604443 23.580466 30.094978 34.861374 55.801049 68.810796 89.559047 75.170577 30.563134 81.081390 22.438910
## [61] 67.721985 86.137954 32.030829 78.882442 37.003271 95.096212 60.150880 15.564809 57.618973 69.665515 90.034839 85.434367
## [73] 85.128040 15.770302 47.244557 76.415901 23.789545 22.724979 69.690059 72.750305 95.853269 56.970538 25.410259 17.201108
## [85] 9.258579 19.694808 92.545295 24.962562 28.535537 86.151814 2.300156 46.558063 27.749812 40.020869 14.463469 32.616518
## [97] 28.311451 59.892753 61.094825 24.773166
plot(x = ru)order(ru)## [1] 1 91 32 15 43 49 85 29 27 6 95 68 74 84 86 10 33 60 78 40 51 77 46 100 88 83 93 39 97 89 21 52
## [33] 58 63 8 96 16 53 50 65 94 13 47 22 92 12 75 34 20 36 54 44 82 41 3 69 14 98 67 99 11 42 2 61
## [65] 5 55 17 70 79 45 4 80 38 57 30 76 37 64 28 59 18 23 24 73 72 62 90 7 31 19 9 56 71 87 35 66
## [97] 25 81 26 48
ru<- ru[order(ru)]
ru## [1] 0.813247 2.300156 3.370775 3.515963 6.247110 7.269671 9.258579 9.519536 13.571551 14.448441 14.463469 15.564809
## [13] 15.770302 17.201108 19.694808 20.100106 20.574142 22.438910 22.724979 23.305806 23.580466 23.789545 24.520998 24.773166
## [25] 24.962562 25.410259 27.749812 28.231896 28.311451 28.535537 29.143809 30.094978 30.563134 32.030829 32.491287 32.616518
## [37] 34.233539 34.861374 35.604443 37.003271 40.020869 40.587486 43.842966 46.478278 46.558063 46.788096 47.244557 49.107972
## [49] 50.471673 52.874291 55.801049 56.235010 56.970538 57.226434 57.473977 57.618973 58.206649 59.892753 60.150880 61.094825
## [61] 64.040092 66.010886 66.461884 67.721985 68.556828 68.810796 69.411440 69.665515 69.690059 70.654066 70.892271 72.750305
## [73] 73.886750 75.170577 75.180364 76.415901 78.062900 78.882442 79.266511 81.081390 82.002651 82.500989 84.414478 85.128040
## [85] 85.434367 86.137954 86.151814 88.168352 88.644241 88.701946 89.298329 89.559047 90.034839 92.545295 94.263583 95.096212
## [97] 95.701496 95.853269 96.304172 98.712801
plot(x = ru)# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd## [1] 48.97608 40.30701 34.09009 50.68485 44.08372 52.37813 50.95712 58.39737 33.44556 38.81133 50.64723 42.20005 64.52618 46.87732
## [15] 38.57086 58.44337 53.19548 66.84184 52.51308 61.84850 39.12974 32.67900 63.65414 48.25100 56.45454 49.21797 45.39117 49.28439
## [29] 30.75582 63.98128 45.08335 63.25681 50.21262 42.62382 48.94985 32.45685 41.29898 58.53396 56.42590 68.00502 42.43100 47.17094
## [43] 47.68343 50.56463 48.80624 38.91547 56.71447 37.89400 62.57559 51.68568 46.35911 57.15304 47.48249 49.19253 45.84921 63.27839
## [57] 35.34911 61.81540 36.42116 45.28074 43.16673 27.47047 48.86829 44.86605 41.68348 51.67232 62.18740 51.51919 39.49949 48.21517
## [71] 46.25539 30.67451 48.12679 54.01139 56.69051 52.34949 38.41202 48.15378 34.40735 54.13284 50.67885 44.42971 51.89951 53.93423
## [85] 41.55622 67.44353 64.19552 55.25710 60.14271 65.32489 44.70846 68.91228 27.49214 29.91552 60.56114 57.19418 49.23820 47.70850
## [99] 38.92475 48.20695
nd <- nd[order(nd)]
nd## [1] 27.47047 27.49214 29.91552 30.67451 30.75582 32.45685 32.67900 33.44556 34.09009 34.40735 35.34911 36.42116 37.89400 38.41202
## [15] 38.57086 38.81133 38.91547 38.92475 39.12974 39.49949 40.30701 41.29898 41.55622 41.68348 42.20005 42.43100 42.62382 43.16673
## [29] 44.08372 44.42971 44.70846 44.86605 45.08335 45.28074 45.39117 45.84921 46.25539 46.35911 46.87732 47.17094 47.48249 47.68343
## [43] 47.70850 48.12679 48.15378 48.20695 48.21517 48.25100 48.80624 48.86829 48.94985 48.97608 49.19253 49.21797 49.23820 49.28439
## [57] 50.21262 50.56463 50.64723 50.67885 50.68485 50.95712 51.51919 51.67232 51.68568 51.89951 52.34949 52.37813 52.51308 53.19548
## [71] 53.93423 54.01139 54.13284 55.25710 56.42590 56.45454 56.69051 56.71447 57.15304 57.19418 58.39737 58.44337 58.53396 60.14271
## [85] 60.56114 61.81540 61.84850 62.18740 62.57559 63.25681 63.27839 63.65414 63.98128 64.19552 64.52618 65.32489 66.84184 67.44353
## [99] 68.00502 68.91228
plot(x = nd)hist(x = nd)hist(nd, breaks = 20, col = "darkgreen")plot(x = density(nd))boxplot(x = nd)boxplot(x = nd, horizontal = T)ggplot2
Lets be honest, the base plots are ugly! The ggplot2
package gives the user to create a better, more visually appealing
plots. Additional packages such as ggbeeswarm and
ggrepel also contain useful functions to add to the
functionality of ggplot2.
ggplot2 - https://ggplot2.tidyverse.org/
Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html
Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/
The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html
library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()mp + geom_point(aes(color = x3, shape = x3), size = 4)mp + geom_line(size = 2)mp + geom_line(aes(color = x3), size = 2)mp + geom_smooth(method = "loess")mp + geom_smooth(method = "lm")xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
rnorm(50, mean = 60, sd = 5)),
group = factor(rep(1:2, each = 50)),
label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")mp + geom_histogram(color = "black", position = "dodge")mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1mp + geom_density(alpha = 0.5)mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")mp + geom_boxplot() + geom_point()mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")library(ggbeeswarm)
mp + geom_quasirandom()mp + geom_quasirandom(aes(shape = group))mp2 <- mp + geom_violin() +
geom_boxplot(width = 0.1, fill = "white") +
geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
common.legend = T, legend = "bottom")Statistics
Handbook of Biological Statistics - http://biostathandbook.com/
R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html
# Prep data
lev_Loc <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
mutate(Location = factor(Location, levels = lev_Loc),
Name = factor(Name, levels = lev_Name))
xx <- dd %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
## <fct> <dbl> <dbl> <dbl>
## 1 ILL 618 AGL 47 79.3 138.
## 2 CDC Maxim AGL 52.5 86.7 134.
## 3 Laird AGL 56.8 76.8 137.
# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF,
color = Name, group = Name, shape = Name)) +
geom_point(size = 2.5, alpha = 0.7) +
geom_line(size = 1, alpha = 0.7) +
theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.
summary(aov(DTF ~ Name * Location, data = dd))## Df Sum Sq Mean Sq F value Pr(>F)
## Name 2 88 44 3.476 0.0395 *
## Location 2 65863 32931 2598.336 < 2e-16 ***
## Name:Location 4 560 140 11.044 2.52e-06 ***
## Residuals 45 570 13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.
If we only have two things to compare, we could do a t-test.
xx <- dd %>%
filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)##
## Welch Two Sample t-test
##
## data: xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -32.18265 -25.48402
## sample estimates:
## mean of x mean of y
## 52.11111 80.94444
DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.
xx <- dd %>%
filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
Location == "Metaponto, Italy") %>%
spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)##
## Welch Two Sample t-test
##
## data: xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.059739 7.059739
## sample estimates:
## mean of x mean of y
## 137.8333 136.8333
DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.
pch Plot
xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
y = rep(5:1, each = 6, length.out = 26),
pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
geom_point(color = "darkred", fill = "darkblue", size = 5) +
geom_text(aes(label = pch), nudge_x = -0.25) +
scale_shape_manual(values = xx$pch) +
scale_x_continuous(breaks = 6:1) +
scale_y_continuous(breaks = 6:1) +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = "Plot symbols in R (pch)",
subtitle = "color = \"darkred\", fill = \"darkblue\"",
x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")R Markdown
Tutorials on how to create an R markdown document like this one can be found here:
- https://rmarkdown.rstudio.com/articles_intro.html
- https://rmarkdown.rstudio.com/lesson-1.html
- https://alexd106.github.io/intro2R/Rmarkdown_intro.html
© Derek Michael Wright